Role summary
Implements AI and Forward Deployed Engineering capabilities across client-facing feature suites, combining hands-on GenAI engineering with solution deployment, integration, configuration, and field feedback loops. The role focuses on building and adapting RAG, GraphRAG, agentic workflows, orchestration integrations, and data-connected AI features so they work reliably in real enterprise environments.
Key responsibilities
- Develop and configure AI-enabled features across assigned client-facing capability suites
- Implement RAG, GraphRAG, prompt workflows, agent routing, and retrieval pipeline integrations
- Work directly with product, business, data, and engineering stakeholders to translate field requirements into deployable technical solutions
- Build dynamic query-generation and graph-aware retrieval capabilities for knowledge-graph-backed use cases
- Integrate AI capabilities with backend APIs, orchestration services, vector stores, enterprise data sources, and platform services
- Validate outputs against golden datasets, acceptance criteria, grounding rules, and enterprise quality expectations
- Support field deployment, configuration, troubleshooting, demos, UAT feedback, and production-readiness activities
- Document solution behavior, deployment assumptions, known limitations, and upgrade paths for future releases
Required skills & experience
- 5+ years hands-on AI, ML, data, backend, or solution engineering experience, with strong Python fundamentals
- Practical experience building GenAI / LLM applications using RAG, GraphRAG, prompt engineering, embeddings, and vector stores
- Experience integrating AI features with enterprise systems, APIs, data pipelines, orchestration layers, or workflow platforms
- Comfortable working in a Forward Deployed Engineering model, including client-facing discovery, rapid prototyping, field configuration, issue triage, and stakeholder demos
- Familiarity with agent frameworks such as LangGraph, LangChain, MCP-based integrations, or comparable orchestration patterns
- Ability to validate AI outputs using golden datasets, regression checks, grounding criteria, and measurable acceptance criteria
- Strong communication skills with the ability to explain technical trade-offs, implementation constraints, risks, and field observations to technical and business stakeholders
Preferred qualifications
Experience delivering AI solutions in regulated, enterprise, healthcare, pharma, manufacturing, or data-governed environments
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